Overview

Dataset statistics

Number of variables9
Number of observations32951
Missing cells2448
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory72.0 B

Variable types

Text2
Numeric7

Alerts

product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with product_height_cm and 2 other fieldsHigh correlation
product_width_cm is highly overall correlated with product_length_cm and 1 other fieldsHigh correlation
product_category_name has 610 (1.9%) missing valuesMissing
product_name_lenght has 610 (1.9%) missing valuesMissing
product_description_lenght has 610 (1.9%) missing valuesMissing
product_photos_qty has 610 (1.9%) missing valuesMissing
product_id has unique valuesUnique

Reproduction

Analysis started2026-02-07 20:09:22.948637
Analysis finished2026-02-07 20:09:26.868180
Duration3.92 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

product_id
Text

Unique 

Distinct32951
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:26.937803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters1054432
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32951 ?
Unique (%)100.0%

Sample

1st row1e9e8ef04dbcff4541ed26657ea517e5
2nd row3aa071139cb16b67ca9e5dea641aaa2f
3rd row96bd76ec8810374ed1b65e291975717f
4th rowcef67bcfe19066a932b7673e239eb23d
5th row9dc1a7de274444849c219cff195d0b71
ValueCountFrequency (%)
2548af3e6e77a690cf3eb6368e9ab61e1
 
< 0.1%
106392145fca363410d287a815be6de41
 
< 0.1%
1e9e8ef04dbcff4541ed26657ea517e51
 
< 0.1%
3aa071139cb16b67ca9e5dea641aaa2f1
 
< 0.1%
96bd76ec8810374ed1b65e291975717f1
 
< 0.1%
cef67bcfe19066a932b7673e239eb23d1
 
< 0.1%
9dc1a7de274444849c219cff195d0b711
 
< 0.1%
d4484cc239fbd0ac671ab04d931edc661
 
< 0.1%
4e1d2ef2974c85d82582edfe594a4f571
 
< 0.1%
d218a47759ef0d1db44044934909b88b1
 
< 0.1%
Other values (32941)32941
> 99.9%
2026-02-07T17:09:27.068294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
866459
 
6.3%
366385
 
6.3%
e66223
 
6.3%
c66221
 
6.3%
966068
 
6.3%
166065
 
6.3%
d66015
 
6.3%
565942
 
6.3%
765894
 
6.2%
f65852
 
6.2%
Other values (6)393308
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1054432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
866459
 
6.3%
366385
 
6.3%
e66223
 
6.3%
c66221
 
6.3%
966068
 
6.3%
166065
 
6.3%
d66015
 
6.3%
565942
 
6.3%
765894
 
6.2%
f65852
 
6.2%
Other values (6)393308
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1054432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
866459
 
6.3%
366385
 
6.3%
e66223
 
6.3%
c66221
 
6.3%
966068
 
6.3%
166065
 
6.3%
d66015
 
6.3%
565942
 
6.3%
765894
 
6.2%
f65852
 
6.2%
Other values (6)393308
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1054432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
866459
 
6.3%
366385
 
6.3%
e66223
 
6.3%
c66221
 
6.3%
966068
 
6.3%
166065
 
6.3%
d66015
 
6.3%
565942
 
6.3%
765894
 
6.2%
f65852
 
6.2%
Other values (6)393308
37.3%

product_category_name
Text

Missing 

Distinct73
Distinct (%)0.2%
Missing610
Missing (%)1.9%
Memory size257.6 KiB
2026-02-07T17:09:27.152568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length32
Mean length14.958659
Min length3

Characters and Unicode

Total characters483778
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowperfumaria
2nd rowartes
3rd rowesporte_lazer
4th rowbebes
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho3029
 
9.4%
esporte_lazer2867
 
8.9%
moveis_decoracao2657
 
8.2%
beleza_saude2444
 
7.6%
utilidades_domesticas2335
 
7.2%
automotivo1900
 
5.9%
informatica_acessorios1639
 
5.1%
brinquedos1411
 
4.4%
relogios_presentes1329
 
4.1%
telefonia1134
 
3.5%
Other values (63)11596
35.9%
2026-02-07T17:09:27.276526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a57759
11.9%
e57376
11.9%
o49486
10.2%
s48323
10.0%
i31889
 
6.6%
_30870
 
6.4%
r29702
 
6.1%
t24536
 
5.1%
c23062
 
4.8%
m21106
 
4.4%
Other values (18)109669
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)483778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a57759
11.9%
e57376
11.9%
o49486
10.2%
s48323
10.0%
i31889
 
6.6%
_30870
 
6.4%
r29702
 
6.1%
t24536
 
5.1%
c23062
 
4.8%
m21106
 
4.4%
Other values (18)109669
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)483778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a57759
11.9%
e57376
11.9%
o49486
10.2%
s48323
10.0%
i31889
 
6.6%
_30870
 
6.4%
r29702
 
6.1%
t24536
 
5.1%
c23062
 
4.8%
m21106
 
4.4%
Other values (18)109669
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)483778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a57759
11.9%
e57376
11.9%
o49486
10.2%
s48323
10.0%
i31889
 
6.6%
_30870
 
6.4%
r29702
 
6.1%
t24536
 
5.1%
c23062
 
4.8%
m21106
 
4.4%
Other values (18)109669
22.7%

product_name_lenght
Real number (ℝ)

Missing 

Distinct66
Distinct (%)0.2%
Missing610
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean48.476949
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:27.324429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median51
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.245741
Coefficient of variation (CV)0.21135284
Kurtosis0.19256346
Mean48.476949
Median Absolute Deviation (MAD)7
Skewness-0.90322176
Sum1567793
Variance104.9752
MonotonicityNot monotonic
2026-02-07T17:09:27.393583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
602182
 
6.6%
592025
 
6.1%
581887
 
5.7%
571719
 
5.2%
551683
 
5.1%
561675
 
5.1%
541439
 
4.4%
531330
 
4.0%
521259
 
3.8%
501039
 
3.2%
Other values (56)16103
48.9%
ValueCountFrequency (%)
52
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
98
< 0.1%
105
 
< 0.1%
117
< 0.1%
1213
< 0.1%
1316
< 0.1%
1411
< 0.1%
ValueCountFrequency (%)
761
 
< 0.1%
721
 
< 0.1%
691
 
< 0.1%
681
 
< 0.1%
671
 
< 0.1%
661
 
< 0.1%
6459
 
0.2%
63515
1.6%
6265
 
0.2%
6165
 
0.2%

product_description_lenght
Real number (ℝ)

Missing 

Distinct2960
Distinct (%)9.2%
Missing610
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean771.49528
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:27.585666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile150
Q1339
median595
Q3972
95-th percentile2063
Maximum3992
Range3988
Interquartile range (IQR)633

Descriptive statistics

Standard deviation635.11522
Coefficient of variation (CV)0.82322632
Kurtosis4.8289228
Mean771.49528
Median Absolute Deviation (MAD)293
Skewness1.9620928
Sum24950929
Variance403371.35
MonotonicityNot monotonic
2026-02-07T17:09:27.664436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40494
 
0.3%
72986
 
0.3%
70366
 
0.2%
65166
 
0.2%
23665
 
0.2%
18465
 
0.2%
30363
 
0.2%
35262
 
0.2%
37560
 
0.2%
24658
 
0.2%
Other values (2950)31656
96.1%
(Missing)610
 
1.9%
ValueCountFrequency (%)
45
< 0.1%
81
 
< 0.1%
151
 
< 0.1%
201
 
< 0.1%
231
 
< 0.1%
262
 
< 0.1%
271
 
< 0.1%
281
 
< 0.1%
302
 
< 0.1%
311
 
< 0.1%
ValueCountFrequency (%)
39921
< 0.1%
39881
< 0.1%
39851
< 0.1%
39761
< 0.1%
39631
< 0.1%
39561
< 0.1%
39542
< 0.1%
39501
< 0.1%
39491
< 0.1%
39481
< 0.1%

product_photos_qty
Real number (ℝ)

Missing 

Distinct19
Distinct (%)0.1%
Missing610
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.1889861
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:27.717209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7367656
Coefficient of variation (CV)0.79341099
Kurtosis7.2635342
Mean2.1889861
Median Absolute Deviation (MAD)0
Skewness2.1934091
Sum70794
Variance3.0163549
MonotonicityNot monotonic
2026-02-07T17:09:27.769506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
116489
50.0%
26263
 
19.0%
33860
 
11.7%
42428
 
7.4%
51484
 
4.5%
6968
 
2.9%
7343
 
1.0%
8192
 
0.6%
9105
 
0.3%
1095
 
0.3%
Other values (9)114
 
0.3%
(Missing)610
 
1.9%
ValueCountFrequency (%)
116489
50.0%
26263
 
19.0%
33860
 
11.7%
42428
 
7.4%
51484
 
4.5%
6968
 
2.9%
7343
 
1.0%
8192
 
0.6%
9105
 
0.3%
1095
 
0.3%
ValueCountFrequency (%)
201
 
< 0.1%
191
 
< 0.1%
182
 
< 0.1%
177
 
< 0.1%
158
 
< 0.1%
145
 
< 0.1%
139
 
< 0.1%
1235
 
0.1%
1146
0.1%
1095
0.3%

product_weight_g
Real number (ℝ)

High correlation 

Distinct2204
Distinct (%)6.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2276.4725
Minimum0
Maximum40425
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:27.833632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1300
median700
Q31900
95-th percentile10850
Maximum40425
Range40425
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation4282.0387
Coefficient of variation (CV)1.8809974
Kurtosis15.133565
Mean2276.4725
Median Absolute Deviation (MAD)500
Skewness3.6048598
Sum75007492
Variance18335856
MonotonicityNot monotonic
2026-02-07T17:09:27.900826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002084
 
6.3%
3001561
 
4.7%
1501259
 
3.8%
4001206
 
3.7%
1001188
 
3.6%
5001112
 
3.4%
2501001
 
3.0%
600957
 
2.9%
350832
 
2.5%
700748
 
2.3%
Other values (2194)21001
63.7%
ValueCountFrequency (%)
04
 
< 0.1%
25
 
< 0.1%
251
 
< 0.1%
50312
0.9%
531
 
< 0.1%
541
 
< 0.1%
552
 
< 0.1%
581
 
< 0.1%
606
 
< 0.1%
611
 
< 0.1%
ValueCountFrequency (%)
404251
 
< 0.1%
30000143
0.4%
298001
 
< 0.1%
297501
 
< 0.1%
297002
 
< 0.1%
296005
 
< 0.1%
295002
 
< 0.1%
292501
 
< 0.1%
291501
 
< 0.1%
291001
 
< 0.1%

product_length_cm
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.815078
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:27.977726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile65
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.914458
Coefficient of variation (CV)0.54890201
Kurtosis3.513618
Mean30.815078
Median Absolute Deviation (MAD)8
Skewness1.7504597
Sum1015326
Variance286.09889
MonotonicityNot monotonic
2026-02-07T17:09:28.041289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165520
 
16.8%
202816
 
8.5%
302029
 
6.2%
181502
 
4.6%
251387
 
4.2%
171310
 
4.0%
191270
 
3.9%
401224
 
3.7%
22972
 
2.9%
35968
 
2.9%
Other values (89)13951
42.3%
ValueCountFrequency (%)
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
103
 
< 0.1%
1116
 
< 0.1%
1215
 
< 0.1%
1320
 
0.1%
1440
 
0.1%
1548
 
0.1%
165520
16.8%
ValueCountFrequency (%)
105149
0.5%
10419
 
0.1%
1039
 
< 0.1%
10219
 
0.1%
10118
 
0.1%
100119
0.4%
9916
 
< 0.1%
9816
 
< 0.1%
977
 
< 0.1%
964
 
< 0.1%

product_height_cm
Real number (ℝ)

High correlation 

Distinct102
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.937661
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:28.108963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q321
95-th percentile44
Maximum105
Range103
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.637554
Coefficient of variation (CV)0.80516158
Kurtosis6.6786189
Mean16.937661
Median Absolute Deviation (MAD)6
Skewness2.1400613
Sum558079
Variance185.98288
MonotonicityNot monotonic
2026-02-07T17:09:28.185630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102548
 
7.7%
152022
 
6.1%
201991
 
6.0%
161595
 
4.8%
111551
 
4.7%
51529
 
4.6%
121522
 
4.6%
81467
 
4.5%
21357
 
4.1%
71235
 
3.7%
Other values (92)16132
49.0%
ValueCountFrequency (%)
21357
4.1%
3888
 
2.7%
41176
3.6%
51529
4.6%
61138
3.5%
71235
3.7%
81467
4.5%
91068
3.2%
102548
7.7%
111551
4.7%
ValueCountFrequency (%)
10524
0.1%
1045
 
< 0.1%
1034
 
< 0.1%
1028
 
< 0.1%
10015
< 0.1%
991
 
< 0.1%
982
 
< 0.1%
972
 
< 0.1%
964
 
< 0.1%
957
 
< 0.1%

product_width_cm
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.196728
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2026-02-07T17:09:28.249289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile47
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.079047
Coefficient of variation (CV)0.52072203
Kurtosis4.0731259
Mean23.196728
Median Absolute Deviation (MAD)6
Skewness1.6709713
Sum764309
Variance145.90339
MonotonicityNot monotonic
2026-02-07T17:09:28.317446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113718
 
11.3%
203053
 
9.3%
162808
 
8.5%
152393
 
7.3%
301786
 
5.4%
121536
 
4.7%
251329
 
4.0%
141264
 
3.8%
131133
 
3.4%
171118
 
3.4%
Other values (85)12811
38.9%
ValueCountFrequency (%)
62
 
< 0.1%
74
 
< 0.1%
89
 
< 0.1%
915
 
< 0.1%
1023
 
0.1%
113718
11.3%
121536
4.7%
131133
 
3.4%
141264
 
3.8%
152393
7.3%
ValueCountFrequency (%)
1181
 
< 0.1%
1055
 
< 0.1%
1041
 
< 0.1%
1031
 
< 0.1%
1022
 
< 0.1%
1012
 
< 0.1%
10013
< 0.1%
981
 
< 0.1%
971
 
< 0.1%
951
 
< 0.1%

Interactions

2026-02-07T17:09:26.192948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.544195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.941677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.378623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.858015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.420266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.791642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.244788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.593169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.009674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.442335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.931609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.468845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.857737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.308195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.645939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.070248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.510008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.999788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.535081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.906041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.368452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.720612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.140645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.558862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.056997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.582846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.976852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.432567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.769614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.202207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.632523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.117146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.643812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.024548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.484385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.824689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.250195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.696612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.180597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.691766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.076001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.532514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:23.877835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.320064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:24.810117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.358790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:25.743528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T17:09:26.139648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-07T17:09:28.365310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
product_description_lenghtproduct_height_cmproduct_length_cmproduct_name_lenghtproduct_photos_qtyproduct_weight_gproduct_width_cm
product_description_lenght1.0000.100-0.0080.1000.1250.108-0.056
product_height_cm0.1001.0000.249-0.039-0.0390.5230.364
product_length_cm-0.0080.2491.0000.0800.0400.6170.613
product_name_lenght0.100-0.0390.0801.0000.1530.0990.068
product_photos_qty0.125-0.0390.0400.1531.0000.0100.002
product_weight_g0.1080.5230.6170.0990.0101.0000.605
product_width_cm-0.0560.3640.6130.0680.0020.6051.000

Missing values

2026-02-07T17:09:26.615813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-07T17:09:26.693112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-07T17:09:26.792300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cm
01e9e8ef04dbcff4541ed26657ea517e5perfumaria40.0287.01.0225.016.010.014.0
13aa071139cb16b67ca9e5dea641aaa2fartes44.0276.01.01000.030.018.020.0
296bd76ec8810374ed1b65e291975717fesporte_lazer46.0250.01.0154.018.09.015.0
3cef67bcfe19066a932b7673e239eb23dbebes27.0261.01.0371.026.04.026.0
49dc1a7de274444849c219cff195d0b71utilidades_domesticas37.0402.04.0625.020.017.013.0
541d3672d4792049fa1779bb35283ed13instrumentos_musicais60.0745.01.0200.038.05.011.0
6732bd381ad09e530fe0a5f457d81becbcool_stuff56.01272.04.018350.070.024.044.0
72548af3e6e77a690cf3eb6368e9ab61emoveis_decoracao56.0184.02.0900.040.08.040.0
837cc742be07708b53a98702e77a21a02eletrodomesticos57.0163.01.0400.027.013.017.0
98c92109888e8cdf9d66dc7e463025574brinquedos36.01156.01.0600.017.010.012.0
product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cm
329416ec96c91757fad0aecafc0ee7f262dccbebes62.01417.01.09550.036.035.035.0
3294216280ca280a86fee2ba3c928ed04439fmoveis_decoracao64.0236.011.02200.031.011.026.0
329433becff10d1deb92b02f2a1ee62a04524informatica_acessorios54.01520.02.06150.030.030.020.0
329441a14237ecc2fe3772b55c8d4e11ccb35moveis_decoracao58.01405.03.0150.035.02.025.0
32945c4e71b64511b959455e2107fe7859020utilidades_domesticas59.01371.02.0200.018.015.015.0
32946a0b7d5a992ccda646f2d34e418fff5a0moveis_decoracao45.067.02.012300.040.040.040.0
32947bf4538d88321d0fd4412a93c974510e6construcao_ferramentas_iluminacao41.0971.01.01700.016.019.016.0
329489a7c6041fa9592d9d9ef6cfe62a71f8ccama_mesa_banho50.0799.01.01400.027.07.027.0
3294983808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.0
32950106392145fca363410d287a815be6de4cama_mesa_banho58.0309.01.02083.012.02.07.0